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Hydraulic Fracturing Case Study
Prevented $4.2M in Equipment Failures with Predictive AI
The Challenge
In hydraulic fracturing, equipment failure is not an inconvenience, it is a customer relationship event. When a frac pump fails mid-stage, the entire spread is down, the operator is paying standby, and someone is on the phone explaining why.
- Pump failures at a rate of 2.3 events per spread per month. Most were preventable in hindsight - vibration anomalies, temperature drift, pressure deviation - but the data wasn't being monitored in real time.
- Maintenance was calendar-based. Pumps were torn down every X hours regardless of actual condition, simultaneously over-maintaining healthy equipment and under-detecting pre-failure signals.
- Knowledge was tribal. The best mechanics could 'feel' a pump going bad. The new mechanics couldn't. When senior mechanics left, the knowledge left with them.
- No closed loop between field data and maintenance decisions. Vibration sensors existed; the data went nowhere actionable.
The Solution
OpsFlo deployed the Predictive Maintenance module with custom ML models trained on 18 months of historical pump data. Implementation took 16 weeks:
- Asset Intelligence consolidating sensor data, maintenance history, and operating conditions per asset
- Predictive Maintenance AI trained on vibration, temperature, pressure, fluid contamination, and run hours
- Work Order Automation for auto-generated maintenance tasks when AI flagged a pre-failure condition
- Inventory & Parts integration so the right parts were staged before the pump came down
- Mobile app for maintenance crews to receive and complete predictive work orders
The Results
After 18 months on the platform:
- $4.2M in prevented equipment failures - calculated by the maintenance team based on pre-failure interventions
- Unplanned NPT dropped 81% - from 41 hours per spread per month to 7.7 hours
- Pump rebuild costs dropped 34% because catastrophic failures were largely eliminated
- MTBF increased 2.7x (mean time between failures)
- One spread now operates with one mechanic on standby instead of two - the predictive workflow shifted work upstream
Key Impacts
$4.2M
Prevented Equipment Failures
81%
Reduction in Unplanned NPT
14mo
Payback on Full Deployment
“Our best mechanics used to say a pump was 'sounding rough' and we'd pull it. They were right 80% of the time. OpsFlo's AI is right 94% of the time, and it works on third-shift Saturday too. We're not replacing our senior mechanics, we're scaling their judgment across the entire fleet.”
Director of Maintenance
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